| 摘 要： |
Currently, deep learning-based synthetic aperture radar (SAR) image ship target detection methods have been widely used in the field of SAR image ship detection. However, these methods suffer from high model complexity and poor performance when detecting small dense targets. To address this problem, this paper proposes a ship target detection algorithm based on the improved YOLO (You Only Look Once) algorithm. In addition, considering the real-time requirements and computational constraints in mobile applications, the YOLOv4 network is modified to make it more lightweight. Moreover, decoupled head and coordinate attention are introduced to preserve YOLOv4's superb detection performance as much as possible after lightweighting it. First, as the detection head of the YOLOv4 degrades the performance, this study decouples the classification and regression tasks. Second, since the channel attention mechanism ignores the spatial position information, coordinate attention is used to obtain long-range dependencies and accurate position information in the spatial domain. Moreover, the effects of the coordinate attention mechanism in different hierarchical YOLOv4 structures are analyzed. Furthermore, on the basis of the YOLOv4 backbone, another lightweight backbone is added to the model structure to improve model detection performance. Experimental results on the SAR ship detection dataset (SSDD) and the high-resolution SAR images dataset (HRSID) demonstrate that the proposed method can achieve high detection accuracy in complex scenes. The proposed lightweight model has fewer parameters compared to the original YOLOv4 structure. Furthermore, two massive SAR images are used to confirm the proposed model's migration application performance. The experimental results demonstrate that the proposed model has a strong migration ability and can be used in maritime monitoring. |
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